ABSTRACT
The objective of this chapter is to present a concise view of deep networks and to familiarize the reader with deep learning-based computing. Deep learning is an emerging area of machine learning (ML) research. It comprises multiple hidden layers of artificial neural networks. The deep learning methodology applies nonlinear transformations and model abstractions of high level in large databases. Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection, and many other domains. The recent advancements in deep learning architectures within numerous fields have already provided significant contributions in artificial intelligence (AI). Intricate structures are developed in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Furthermore, the superior and beneficial nature of the deep learning methodology and its hierarchy in layers and nonlinear operations are presented and compared with more conventional algorithms in common applications. The chapter further provides a general overview on the concept and the ever-expanding advantages and popularity of deep learning.
